Generally, relational data in graph form can be used to represent networks in many different types of domains, such as Internet-related networks, in the fields of science and research collaboration, in epidemiology related to the study of patterns and causes of health conditions, communication analysis between members and groups in social networks, advertising and marketing analytics, in the study of ecosystems, power grid dynamics, and many others. The network entities in many of these types of complex networks are represented as nodes in a graph. Generally, the links or connections between the network entities may be represented as any form of citations, collaborations, associations, functions, communications, co-locations, shared mechanisms, or many other explicit or implicit relationships. Further, graphs that represent networks can be derived from most any type of data, including non-relational types of data, images data, or other types of traditional data. Generally, the graphs can be derived by computing a similarity (or more generally a function) between every two data points in a graph that captures whether the two data points should be linked in the graph or not.
Notably, the success of many graph-based machine learning tasks depend on an appropriate representation of a complex network, as determined from the graph data, such as for modeling user behavior, detecting unusual user activity (anomaly detection), and entity resolution (network alignment). Conventional techniques typically rely on learning the features of graph nodes simply based on how close the nodes are to one another in the graph, such as determined from a simple adjacency matrix of the graph. For instance, the conventional techniques may incorrectly represent nodes as being similar, despite the nodes having fundamentally different connectivity patterns in the graph, and the conventional techniques are unable to determine the connectivity patterns among the nodes and links in a graph that represents a complex network.